A QSAR–ICE–SSD model prediction of the PNECs for alkylphenol substances and application in ecological risk assessment for rivers of a megacity

Hong, Yajun; Feng, Chenglian; Jin, Xiaowei; Xie, Huiyu; Liu, Na; Bai, Yingchen; Wu, Fengchang; Raimondo, Sandy

doi:10.1016/j.envint.2022.107367

Cited by 29 publications

(12 citation statements)

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“…In the absence of experimental toxicity data, ecotoxicity endpoints should be predicted with the use of validated quantitative structure-activity relationship (QSAR) models, covering a wider range of taxa to allow derivation of PNECs of higher relevance and reliability. Comprehensive experimental datasets covering a well-de ned chemical domain are essential to develop reliable QSAR models for PNEC derivation [39]. Data from in vitro assays e.g.…”

Section: Application To a Case Study On Wastewater E Uent Samplesmentioning

confidence: 99%

Beyond Target Chemicals: Updating the NORMAN Prioritisation Scheme to Support the EU Chemical Strategy with Semi-quantitative Suspect/Non-target Screening Data

Dulio,

Alygizakis,

et al. 2024

Preprint

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Background: Prioritisation of chemical pollutants is a major challenge for environmental managers and decision-makers alike, which is essential to help focus the limited resources available for monitoring and mitigation actions on the most relevant chemicals. This study extends the original NORMAN prioritisation scheme beyond target chemicals, presenting the integration of semi-quantitative data from retrospective suspect screening and expansion of existing exposure and risk indicators. The scheme utilises data retrieved automatically from the NORMAN Database System (NDS), including candidate compounds for prioritisation, target and suspect screening data, ecotoxicological effect data, physico-chemical data and other properties. Two complementary workflows using target and suspect screening monitoring data are applied to first group the substances into six action categories and then rank the compounds using exposure, hazard and risk indicators. The results from the ‘target’ and ‘suspect screening’ workflows can then be combined as multiple lines of evidence to support decision-making on regulatory and research actions. Results: As a proof-of-concept, the new scheme was applied to a combined dataset of target and suspect screening data. To this end, 65,690 compounds on the NDS, of which 2,579 substances supported by target wastewater monitoring data, were retrospectively screened in 84 effluent wastewater samples, totalling >11 million data points. The final prioritisation results identified 577 compounds as high priority for further actions, 45,462 as medium priority and 308 with potentially lower priority for actions, while it was not possible to conclude for 18,000 compounds due to insufficient information from target monitoring and uncertainty in the identification from suspect screening. A high degree of agreement was observed between the categories assigned via target analysis and suspect screening-based prioritisation. Suspect screening was a valuable complementary approach to target analysis, helping to prioritise thousands of compounds that are insufficiently investigated in current monitoring programmes. Conclusions: This updated prioritisation workflow responds to the increasing use of suspect screening techniques. It can be adapted to different environmental compartments and can support regulatory obligations, including the identification of specific pollutants in river basins and the marine environments, as well as the confirmation of environmental occurrence levels predicted by modelling tools.

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Section: Application To a Case Study On Wastewater E Uent Samplesmentioning

confidence: 99%

Beyond Target Chemicals: Updating the NORMAN Prioritisation Scheme to Support the EU Chemical Strategy with Semi-quantitative Suspect/Non-target Screening Data

Dulio,

Alygizakis,

et al. 2024

Preprint

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“…In the cases of different endpoints for the same species, the most sensitive endpoints were selected. In addition, if multiple toxicity values were available for the same endpoint and species, their geometric mean was calculated [12,13].…”

Section: Toxicity Data Collection and Selectionmentioning

confidence: 99%

“…Water-quality criteria (WQC) refer to the highest acceptable concentration or level of pollutants or harmful factors in the water environment, above which the pollutant will have adverse effects on human health, aquatic ecosystems, and their useful functions [6][7][8][9][10]. The objective of WQC is to protect commercially and recreationally important aquatic organisms and other important species, such as fish, invertebrates, and plankton, from the adverse effects caused by short-term exposure to high concentrations or long-term exposure to low concentrations of pollutants [11][12][13]. Environmental protection standards provide an important basis and criteria for environmental law enforcement and management, and the scientificity and rationality of their formulation process directly affect the effect of environmental management and environmental law enforcement [14].…”

Section: Introductionmentioning

confidence: 99%

Freshwater Water-Quality Criteria for Chloride and Guidance for the Revision of the Water-Quality Standard in China

Hong

Zhu

Liao

et al. 2023

IJERPH

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The chloride in water frequently exceeds the standard; directly quoting foreign water-quality criteria (WQC) or standards will inevitably reduce the scientific value of the water-quality standard (WQS) in China. Additionally, this may lead to the under- or overprotection of water bodies. This study summarized the sources, distribution, pollution status, and hazards of chloride in China’s water bodies. Additionally, we compared and analyzed the basis for setting WQS limits for chloride in China; we systematically analyzed the basis for setting the WQC for chloride in foreign countries, especially the United States. Finally, we collected and screened data on the toxicity of chloride to aquatic organisms; we also used the species sensitivity distribution (SSD) method to derive the WQC value for chloride, which is 187.5 mg·L−1. We put forward a recommended value for freshwater WQS for chloride in China: less than 200 mg·L−1. The study of a freshwater WQC for chloride is not only a key point of environmental research, but also an urgent demand to ensure water ecological protection in China. The results of this study are of great significance for the environmental management of chloride, protection of aquatic organisms, and risk assessment, especially for the revision of WQSs.

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“…Numerous anthropic molecules have entered the environment and aroused extensive concern in the scientific community and among the public. − The recorded and unexpected bioactivities of the anthropic molecules and their transformation products impart great threats on ecological safety and human health, even though they are always present at trace concentration levels in the environment. − Identifying these contaminants requires not only resolving their structures but also determining their bioactivities. ,,, Nowadays, the coupling of high-performance liquid chromatography (HPLC) with high-resolution mass spectrometry (HRMS) is proven powerful for resolving the structures of various contaminants present in complicated environmental matrices. , Meanwhile, many quantitative structure–activity relationship (QSAR) models are demonstrated to be viable for predicting the bioactivities of environmental contaminants. , Nonetheless, resolving the exact structures of a large quantity of detected features prior to QSAR modeling is a time-consuming task. And the dominant time is usually wasted on analyzing the structures of the features that turn out to be not bioactive at last.…”

mentioning

confidence: 99%

“…8,9 Meanwhile, many quantitative structure−activity relationship (QSAR) models are demonstrated to be viable for predicting the bioactivities of environmental contaminants. 10,11 Nonetheless, resolving the exact structures of a large quantity of detected features prior to QSAR modeling is a time-consuming task. And the dominant time is usually wasted on analyzing the structures of the features that turn out to be not bioactive at last.…”

mentioning

confidence: 99%

Noncovalent Tagging for Identifying Unknown Contaminants of Specific Bioactivity in Environmental Water

Wen,

Shen,

Lai

et al. 2023

Anal. Chem.

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Identifying contaminants of specific bioactivities from complicated environmental matrices remains costly and time-consuming, as it requires us to not only resolve their structures but also determine their bioactivities. Herein, a novel noncovalent tagging method is integrated in mass spectrometry for identifying unknown contaminants that target dopamine (DA) receptors. Via proteolysis of bovine serum albumin, a stereoselective hexapeptide (ACFAVE) is selected for noncovalently tagging the contaminants that possess the stereostructural characteristics of binding to DA receptors. The tagged contaminants can be readily distinguished from the coexisting species for subsequent structural analysis based on the tagging-induced shifts of the mass-to-charge ratios. Thus, both bioactivity evaluation and structure analysis are accomplished via mass spectrometry. By using this method, 1,3-diphenylguanidine (DPG), a widely used additive in rubber and plastics, is successfully identified out of 2495 features detected in the Pearl River water, with its concentration determined as only 9.8 μg L–1. Furthermore, DPG is confirmed as a potential disrupter to the DA receptors via a simulated docking experiment, which has not been reported before. The present noncovalent tagging method provides a cost-effective and time-efficient way of identifying bioactive molecules in complicated matrices. And proteolysis of proteins is promising for developing more taggants with other desired stereoselectivities in the future.

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A QSAR–ICE–SSD model prediction of the PNECs for alkylphenol substances and application in ecological risk assessment for rivers of a megacity

Cited by 29 publications

References 100 publications

Beyond Target Chemicals: Updating the NORMAN Prioritisation Scheme to Support the EU Chemical Strategy with Semi-quantitative Suspect/Non-target Screening Data

Beyond Target Chemicals: Updating the NORMAN Prioritisation Scheme to Support the EU Chemical Strategy with Semi-quantitative Suspect/Non-target Screening Data

Freshwater Water-Quality Criteria for Chloride and Guidance for the Revision of the Water-Quality Standard in China

Noncovalent Tagging for Identifying Unknown Contaminants of Specific Bioactivity in Environmental Water

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